Triple
T26809825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Passion by Elizabeth Taylor |
E671955
|
entity |
| Predicate | associatedCelebrity |
P107322
|
FINISHED |
| Object | Elizabeth Taylor |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Elizabeth Taylor | Statement: [Passion by Elizabeth Taylor, associatedCelebrity, Elizabeth Taylor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCelebrity Context triple: [Passion by Elizabeth Taylor, associatedCelebrity, Elizabeth Taylor]
-
A.
celebrityMatch
Indicates a relationship where one entity is identified as a suitable or corresponding celebrity counterpart or pairing for another entity.
-
B.
namedForNotablePersonFrom
Indicates that one entity is named in honor of a notable person who originates from another specified place or group.
-
C.
famousTogetherWith
Indicates that two entities share fame or public recognition in association with each other, such that their notability is linked or commonly referenced together.
-
D.
notableStar
Indicates that the subject is a star (or stellar object) that is distinguished or noteworthy in some significant way, such as brightness, fame, or scientific interest, relative to other stars.
-
E.
linkedToFilmStar
chosen
Indicates that one entity has a direct connection or association with a film star.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69eeb3225a3c8190aaf6746efeded2f3 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69ff795d25d08190b7584c72be39d309 |
completed | May 9, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69ff78a90fbc8190a62c57456dc1d4ad |
completed | May 9, 2026, 6:10 p.m. |
Created at: April 27, 2026, 4:28 a.m.